This paper presents the success story of the intuitive vision of an Information and Communication Technology (ICT) high school educator in South Africa. The growth and evolution of a Community of Practice towards a full-fledged living lab is investigated. A grounded theory study analyses the living lab concept and highlights some of the current challenges secondary high school ICT education face within the South African educational landscape. Some of the concepts, ideas, best practices, and lessons learned in the establishment and running of two web based technologies to support secondary school ICT subjects is discussed. The researchers present a motivation for the use of living labs to address some of the issues identified and highlights how the existing platforms fits into bigger design.

One effective way of accelerating the commercialization of university innovations (inventions) is to execute a “Technological Entrepreneurship” framework that helps the execution of agreements between universities and industry for commercialization. Academics have been encouraged to commercialize their research and findings yet the level of success of commercialization of inventions (innovations) in industry is questionable. As there is no agreed commercialization framework to guide the execution of processes to support inventions moving from laboratories to the right market. The lack of capabilities of appropriate processes have undermined the turning of innovation and products into wealth. The research questions are designed to identify the constraints and hindrances of commercialization and the characteristics of successful processes built from framework based on selected case studies of incubation capabilities within universities commercialization program.

This paper presents a validation research of seven Digital Learning Domains (DLDs) and sixty-five performance statements (PSs) as perceived by students with experience in learning via ICT. The preliminary findings suggest a statistical firmness of the inventory. The seven DLDs identified are Social Responsibility, Team-based Learning, Information Research and Retrieval, Information Management, Information Validation, Processing and Presentation of Information, and Digital Integrity. The 65 PSs will enable a teacher to identify the level of competency the learner has in each DLD, thus identifying students’ strengths and weaknesses that must be addressed in order to facilitate learning in the current era. As can be concluded from the findings, most of the participants evaluate themselves as digitally literate with regard to the basic information research and retrieval skills, validation and information management. But when it comes to PSs that require complex decision making or higher order thinking strategies, it seems that a large number of participants lack these skills. Also, social responsibility and digital integrity domains are perceived as known by the participants but not very well taken in terms of pro-active action to enforce appropriate digital behavior, or avoiding illegally obtained music or movies.

Aim/Purpose : Share research finding about ransomware, depict the ransomware work in a format that commonly used by researchers and practitioners and illustrate personal case experience in dealing with ransomware. Background: Author was hit with Ransomware, suffered a lot from it, and did a lot of research about this topic. Author wants to share findings in his research and his experience in dealing with the aftermath of being hit with ransomware. Methodology: Case study. Applying the literature review for a personal case study. Contribution: More knowledge and awareness about ransomware, how it attacks peoples’ computers, and how well informed users can be hit with this malware. Findings: Even advanced computer users can be hit and suffer from Ransomware attacks. Awareness is very helpful. In addition, this study drew in chart format what is termed “The Ransomware Process”, depicting in chart format the steps that ransomware hits users and collects ransom. Recommendations for Practitioners : Study reiterates other recommendations made for dealing with ransomware attacks but puts them in personal context for more effective awareness about this malware. Recommendation for Researchers: This study lays the foundation for additional research to find solutions to the ransomware problem. IT researchers are aware of chart representations to depict cycles (like SDLC). This paper puts the problem in similar representation to show the work of ransomware. Impact on Society: Society will be better informed about ransomware. Through combining research, illustrating personal experience, and graphically representing the work of ransomware, society at large will be better informed about the risk of this malware. Future Research: Research into solutions for this problem and how to apply them to personal cases.

Aim/Purpose: To monitor Sustainability Development Goals (SDG) established by the United Nations through the hierarchical architecture of informing systems Background: The paper discusses the case of Poland and its Gdansk region Contribution: The solution combines the big-picture of civilization with small-picture of a nation, regions, cities, and firms Findings: The presented solution can be implemented if the political will can be secured. Recommendations for Practitioners: Take the main idea of this paper and adapt to your local case. Recommendation for Researchers: Develop some prototypes of presented informing systems and test in your local environment Impact on Society: The success of the sustainability of globalizing society can be secured if the coherent informing systems can be applied to the planning, monitoring, and implementation of the UN's universal SDG. Future Research: Work on the modeling of costs and benefits of the presented solution.

Aim/Purpose: This exploratory study investigates the influential factors of users’ decisions in the dilemma of whether to agree to online personalization or to protect their online privacy. Background: Various factors related to online privacy and anonymity were considered, such as user’s privacy concern on the Web in general and particularly on social networks, user online privacy literacy, and field of study. Methodology: To this end, 155 students from different fields of study in the Israeli academia were administered closed-ended questionnaires. Findings: The multivariate linear regression analysis showed that as the participants’ privacy concern increases, they tend to prefer privacy protection over online personalization. In addition, there were significant differences between men and women, as men tended to favor privacy protection more than women did. Impact on Society: This research has social implications for the academia and general public as they show it is possible to influence the personalization-privacy tradeoff and encourage users to prefer privacy protection by raising their concern for the preservation of their online privacy. Furthermore, the users’ preference to protect their privacy even at the expense of their online malleability may lead to the reduction of online privacy-paradox behavior. Future Research: Since our results were based on students' self-perceptions, which might be biased, future work should apply qualitative analysis to explore additional types and influencing factors of online privacy behavior.

Aim/purpose The purpose of this article is to discuss the psychosocial and emotional outcomes of an introductory health science workshop designed to support and assist incoming health science students before starting their university study.   Background For the past two decades, a South Australian university offered an on-campus face to face workshop titled ‘Preparation for Health Sciences’ to incoming first-year students from eleven allied health programs such as Nursing, Physiotherapy and Medical Imaging. While many were locals, a good number came from regional and rural areas, and many were international students also. They consisted of both on-campus and off-campus students.   The workshop was created as a new learning environment that was available for students of diverse age groups, educational and cultural backgrounds to prepare them to study sciences. The content of the four-day workshop was developed in consultation with the program directors of the allied health programs. The objectives were to: introduce the assumed foundational science knowledge to undertake health sciences degree; gain confidence in approaching science subjects; experience lectures and laboratory activities; and become familiar with the University campus and its facilities. The workshop was delivered a week before the orientation week, before first-year formal teaching weeks. The topics covered were enhancing study skills, medical and anatomical terminology, body systems, basic chemistry and physics, laboratory activities, and assessment of learning.   Methodology In order to determine the outcomes of the workshop, a survey was used requiring participants to agree or disagree about statements concerning the preparatory course and answer open-ended questions relating to the most important information learned and the best aspects of the workshop. Several students piloted this questionnaire before use in order to ascertain the clarity of instructions, terminology and statements. The result of the 2015-2018 pre- and post-evaluation showed that the workshop raised confidence and enthusiasm in commencing university and that the majority considered the workshop useful overall. The findings of the survey are drawn upon to examine the psychosocial and emotional impacts of the workshop on participants. Using secondary qualitative analysis, the researchers identified the themes relating to the psychosocial and emotional issues conveyed by the participants.   Contribution The contributions of the article are in the areas of improving students’ confidence to complete their university degrees and increasing the likelihood of academic success. Findings Of the 285 students who participated in the workshops from 2015 to 2018, 166 completed the survey conducted at the conclusion of the initiative, representing a 58% response rate. The workshops achieved the objectives outlined at the outset. While there were many findings reported (Thalluri, 2016), the results highlighted in this paper relate to the psychosocial and emotional impacts of the workshop on students. Three themes emerged, and these were Increased preparedness and confidence; Networking and friendships that enhanced support, and Reduced anxiety to study sciences. Some drawbacks were also reported including the cost, time and travel involved. Recommendations for practitioners Students found the introductory workshop to be psychosocially and emotionally beneficial. It is recommended that the same approach be applied for teaching other challenging fields such as mathematics and physics within the university and in other contexts and institutions. Recommendations for researchers Improving and extending the workshop to provide greater accessibility and autonomy is recommended. A longitudinal study to follow up the durability of the workshop is also proposed. Impact on society The impacts in the broader community include: higher academic success for students; improved mental health due to social networking and friendship groups and reduced anxiety and fear; reduced dropout rate in their first year; greater potential to complete educational degrees; reduced wastage in human and financial resources; and increased human capital. Future research Addressing the limitations of cost, time and travel involved, and following-up with the participants’ academic and workplace performance are future directions for research.

Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.

Aim/Purpose: Designing augmented reality (AR) experiences for education, health or entertainment involves multidisciplinary teams making design decisions across several areas. The goal of this paper is to present a classification schema that describes the design choices when constructing an AR interactive experience. Background: Existing extended reality schema often focuses on single dimensions of an AR experience, with limited attention to design choices. These schemata, combined with an analysis of a diverse range of AR applications, form the basis for the schema synthesized in this paper. Methodology: An extensive literature review and scoring of existing classifications were completed to enable a definition of seven design dimensions. To validate the design dimensions, the literature was mapped to the seven-design choice to represent opportunities when designing AR iterative experiences. Contribution: The classification scheme of seven dimensions can be applied to communicating design considerations and alternative design scenarios where teams of domain specialists need to collaborate to build AR experiences for a defined purpose. Findings: The dimensions of nature of reality, location (setting), feedback, objects, concepts explored, participant presence and interactive agency, and style describe features common to most AR experiences. Classification within each dimension facilitates ideation for novel experiences and proximity to neighbours recommends feasible implementation strategies. Recommendations for Practitioners: To support professionals, this paper presents a comprehensive classification schema and design rationale for AR. When designing an AR experience, the schema serves as a design template and is intended to ensure comprehensive discussion and decision making across the spectrum of design choices. Recommendations for Researchers: The classification schema presents a standardized and complete framework for the review of literature and AR applications that other researchers will benefit from to more readily identify relevant related work. Impact on Society: The potential of AR has not been fully realized. The classification scheme presented in this paper provides opportunities to deliberately design and evaluate novel forms of AR experience. Future Research: The classification schema can be extended to include explicit support for the design of virtual and extended reality applications.

Aim/Purpose. This study examined the self-perception of adolescents and young people aged 17-21 – how they perceived their personal characteristics, self-image, vitality, belonging to a local and global (glocal) society, happiness index and activity, media usage habits in general and smartphones in particular – in other words, it sought to produce a sketch of their character. Background. Different age groups are influenced by various factors that shape them, including living environment, technological developments, experiences, common issues, events of glocal significance, and more. People belonging to Gen Z were born at the end of the previous century and the beginning of the 21st century (up to 2010). This generation was born into the digital technological age and is the first one born into the environment defined by smartphones, and social media. Its members are referred to as “digital natives” because they were born after the widespread adoption of digital technology in the Western world. They entered an environment characterized by the widespread daily use of smartphones, the Internet, and technology in general. Methodology. This was a quantitative study based on a sample of 418 Israeli adolescents and young people aged 17-21. The following questionnaires were administered anonymously and disseminated online to an audience of youths aged 17-21 across Israel: A demographic questionnaire; Self-esteem; Vitality; Belonging vs. alienation; Social-emotional aspects; Usage habits in digital environments; Usage habits of learning on a smartphone; Open questions. Contribution. The current study tried to define clusters to characterize adolescents and youth aged 17-21. Findings Results show that study participants had high self-esteem and vitality, felt be-longing, happy, and satisfied with their life, and perceived themselves as active and enterprising at an average level or above. The study identified two clusters. Participants in Cluster 1 were characterized by higher parameter averages than those in Cluster 2 on the self-image, vitality, belonging, happiness, and activism scales. Participants in Cluster 1 felt that using a smartphone made life easier, helped them solve everyday problems, made everyday conduct easier, and allowed them to express themselves, keep up to date with what is happening with their friends, disseminate information conveniently, be involved in social life, and establish relationships with those around them. They thought that it was easy to collaborate with others and to plan activities and events. Recommendations for Practitioners. When examining cluster correlations with data in relation to other variables, it is apparent that participants in Cluster 1 had more options to reach out for help, report more weekly hours spent talking and meeting with friends and feel that using a smartphone makes everyday life easier and facilitates their day-to-day conduct than did participants in Cluster 2. The smartphone allows them to express themselves, keep updated regarding what is happening with their friends and disseminate information easily, helps them be involved in social life and establish connections with those around them. They find it easy to communicate and cooperate with others and to plan activities and events. By contrast, participants in Cluster 2 felt that the smartphone complicates things for them and creates problems in their daily lives. They feel that the use of social networks burdens them and that the smartphone prevents them from being more involved in their social life, and from establishing relationships with those around them. They felt that communication by smartphone creates more problems in understanding messages. Recommendations for Researchers. One of the challenges of this generation is forming an independent identity and self-regulation in a digital, global, across-the-border era that offers a variety of possibilities and communities. They must examine the connection between the digital and personal spaces, to be able to enjoy virtual communities and a sense of togetherness, and at the same time maintain privacy, autonomy, and individuality. Many studies point to the blurring of boundaries between the private-personal and the public, at numerous problems in social networks, including social problems, shaming, and exclusion from various groups and activities. The fear of shaming and the desire to keep up with everything that is happening create a state of mental stress, and adolescents often feel that they urgently need to check their smartphones. Sharing with others can help them deal with negative content and experiences and avoid the dangers lurking in their web surfing. Yet sharing, especially with friends, often causes intimate content to become public and leads to shaming and invasion of privacy. Impact on Society. Gen Z was born into an environment where smartphones, the Internet, and technology in general, are widely used in everyday routine, and they make extensive use of technological means in all areas of life. One of the characteristics of this generation is “globalization.” The present study showed that about 84% of participants felt to a moderate degree or higher that they were citizens of the world. Future Research. The findings of this study revealed a significant difference in self-image between males and females. An attempt was made to explain the findings in light of previous studies, but the need arose for studies on the self-image of young people of Gen Z that would shed light on the subject.

Aim/Purpose. The problem statement in the proposed study focuses on that, despite the growing recognition that teenagers need to undergo security awareness training, little is known about the impacts security training experts believe implementing a mandatory gamified security awareness training curriculum in public middle schools will have on the long-term security behavior of students in Texas. Background. This study was guided by the research question: What are the impacts security training experts believe implementing a mandatory gamified security aware-ness training curriculum in public middle schools will have on the long-term security behaviors of students in Texas? The study gathers opinions from experts on the impacts of security awareness training on students. Methodology. Our research used semi-structured interviews with twelve experts chosen through the use of purposive sampling. The population for the study consisted of experts in the fields of security awareness training for and teaching middle school-aged children. Candidates were recruited through the Cyber-Texas Foundation and snowball sampling techniques. Contribution. The research contributed to the body of knowledge by using interviews to explore the impacts of security awareness training on middle school students based on the opinions and views of the teachers and instructors who work with middle school students. Findings. The findings of this study demonstrate that middle school is an ideal time to provide cybersecurity training and will impact student behaviors by making them more conscious of cyber threats and preparing them to be more tech-savvy professionals. The research also showed that well-designed cybersecurity games with real-world application combined with traditional teaching techniques can help students develop positive habits. The research also suggests that teachers possess the skills to teach cybersecurity classes and the classes can be integrated into the current school day without the need for any significant changes to existing daily schedules. Recommendations for Practitioners. A well-design gamification-based curriculum implemented in Texas Middle Schools, combined with traditional teaching techniques and repeated over an extended time period, will impact students’ behaviors by making them more able to recognize and respond to cyber risks and will transform them into more secure and tech-savvy members of society. Recommendations for Researchers. The research shows middle school instructors and technology experts believe the implementation of a security awareness training program in middle schools is both possible and practical, while also beneficial to the students. The recommendation is to encourage researchers to explore ways to build curricula and games capable of appealing to students and implementing the instruction into school programs. Impact on Society. Demonstrating that training provided in middle school will make lasting impacts and improvements to student behaviors benefits children and their families in the short-term and workplaces in the long-term. The development of a more security-conscious workforce can reduce the significant number of data breaches and cyber attacks resulting from the poor security habits of companies’ users. Future Research. Future research that will add significant value to the body of knowledge includes testing the effectiveness of habit-shaping games to determine whether existing long-term games maintain student interest. Qualitative studies could interview parents of teenagers using habit-shaping games to determine the effectiveness of the applications. Another qualitative study could interview teachers to determine how teachers’ ages affect their comfort level teaching technology classes. Both studies could provide valuable insights into how to implement security awareness training in schools.

Collaboration technologies enable people to communicate and use information to make organizational decisions. The United States Navy refers to this concept as information dominance. Various collaboration technologies are used by the Navy to achieve this mission. This qualitative descriptive study objectively examined how a matrix oriented Navy activity perceived an implemented collaboration technology. These insights were used to determine whether a specific collaboration technology achieved a mission of information dominance. The study used six collaboration themes as a foundation to include: (a) Cultural intelligence, (b) Communication, (c) Capability, (d) Coordination, (e) Cooperation, and (f) Convergence. It was concluded that collaboration technology was mostly perceived well and helped to achieve some levels of information dominance. Collaboration technology improvement areas included bringing greater awareness to the collaboration technology, revamping the look and feel of the user interface, centrally paying for user and storage fees, incorporating more process management tools, strategically considering a Continuity of Operations, and incorporating additional industry best practices for data structures. Emerging themes of collaboration were collected to examine common patterns identified in the collected data. Emerging themes included acceptance, awareness, search, scope, content, value, tools, system performance, implementation, training, support, usage, structure, complexity, approach, governance/configuration management/policy, and resourcing.

This project shows a creative approach to the familiar scavenger hunt game. It involved the implementation of an iPhone application, HUNT, with Augmented Reality (AR) capability for the users to play the game as well as an administrative website that game organizers can use to create and make available games for users to play. Using the HUNT mobile app, users will first make a selection from a list of games, and they will then be shown a list of objects that they must seek. Once the user finds a correct object and scans it with the built-in camera on the smartphone, the application will attempt to verify if it is the correct object and then display associated multi-media AR content that may include images and videos overlaid on top of real world views. HUNT not only provides entertaining activities within an environment that players can explore, but the AR contents can serve as an educational tool. The project is designed to increase user involvement by using a familiar and enjoyable game as a basis and adding an educational dimension by incorporating AR technology and engaging and interactive multimedia to provide users with facts about the objects that they have located

The purpose of the study is to identify influential factors in the use of collaborative networks within the context of manufacturing. The study aims to investigate factors that influence employees’ learning, and to bridge the gap between theory and praxis in collaborative networks in manufacturing. The study further extends the boundary of a collaborative network beyond enterprises to include suppliers, customers, and external stakeholders. It provides a holistic perspective of collaborative networks within the complexity of the manufacturing environment, based on empirical evidence from a questionnaire survey of 246 respondents from diverse manufacturing industries. Drawing upon the socio-technical systems (STS) theory, the study presents the theoretical context and interpretations through the lens of manufacturing. The results show significant influences of organizational support, promotive interactions, positive interdependence, internal-external learning, perceived effectiveness, and perceived usefulness on the use of collaborative networks among manufacturing employees. The study offers a basis of empirical validity for measuring collaborative networks in organizational learning and knowledge/information sharing in manufacturing.

Aim/Purpose: The present study investigates the relationship between the five-factor model (FFM) of personality traits and sales performance in Pakistan. Background: Personality is a well-researched area in which numerous studies have examined the correlation between personality traits and job performance. In this study, a positive effect between the various dimensions of the five-factor model (extraversion, agreeableness, conscientiousness, emotional stability, and open to experience) and sales performance in Pakistan is investigated. Methodology: Pearson’s correlation values as well as analysis methodologies were employed to gather descriptive statistics, reliability analysis, correlation analysis, and use the analytical hierarchy process (AHP). Cronbach’s alpha value helped determine the internal consistency of the group items. Questionnaires were distributed among 600 salespersons in various cities of Pakistan from April 2015 to January 2016. Subsequently, 510 questionnaires were acquired for the sample. Contribution: The current study contributes to the literature on personality traits and sales performance by applying empirical evidence from sales managers in three industries of Pakistan: pharmaceutical, insurance, and electronics. Findings: The results affirmed a positive effect of the five-factor model on sales performance among various industries in Pakistan. The effect of each sub-factor from the five-factor model was examined autonomously. There is a favorable benefit to sales managers in considering FFM when making hiring decisions. Impact on Society: FFM offers important insights into personality traits that work well within Pakistani sales industry structure. Future Research: A broader rendering of the effects of FFM on sales organizations in other geographical locations around Pakistan should be considered. Additionally, an extended study should be conducted to investigate the effects of FFM on female sales employees involving religious and cultural forces within that country.

Aim/Purpose: This journal paper seeks to understand historical aspects of data management, leading to the current data issues faced by organizational executives in relation to big data and how best to present the information to circumvent big data challenges for executive strategic decision making. Background: This journal paper seeks to understand what executives value in data visualization, based on the literature published from prior data studies. Methodology: The qualitative methodology was used to understand the sentiments of executives and data analysts using semi-structured interview techniques. Contribution: The preliminary findings can provide practical knowledge for data visualization designers, but can also provide academics with knowledge to reflect on and use, specifically in relation to information systems (IS) that integrate human experience with technology in more valuable and productive ways. Findings: Preliminary results from interviews with executives and data analysts point to the relevance of understanding and effectively presenting the data source and the data journey, using the right data visualization technology to fit the nature of the data, creating an intuitive platform which enables collaboration and newness, the data presenter’s ability to convey the data message and the alignment of the visualization to core the objectives as key criteria to be applied for successful data visualizations Recommendations for Practitioners: Practitioners, specifically data analysts, should consider the results highlighted in the findings and adopt such recommendations when presenting data visualizations. These include data and premise understanding, ensuring alignment to the executive’s objective, possessing the ability to convey messages succinctly and clearly to the audience, having knowledge of the domain to answer questions effectively, and using the right technology to convey the message. Recommendation for Researchers: The importance of human cognitive and sensory processes and its impact in IS development is paramount. More focus can be placed on the psychological factors of technology acceptance. The current TAM model, used to describe use, identifies perceived usefulness and perceived ease-of-use as the primary considerations in technology adoption. However, factors that have been identified that impact on use do not express the importance of cognitive processes in technology adoption. Future Research: Future research requires further focus on intangible and psychological factors that could affect technology adoption and use, as well as understanding data visualization effectiveness in corporate environments, not only predominantly within the Health sector. Lessons from Health sector studies in data visualization should be used as a platform.

Aim/Purpose: A vital business activity within socio-technical enterprises is tacit knowledge externalization, which elicits and explicates tacit knowledge of enterprise employees as external knowledge. The aim of this paper is to integrate diverse aspects of externalization through the Enterprise Ontology model. Background: Across two decades, researchers have explored various aspects of tacit knowledge externalization. However, from the existing works, it is revealed that there is no uniform representation of the externalization process, which has resulted in divergent and contradictory interpretations across the literature. Methodology : The Enterprise Ontology model is constructed step-wise through the conceptual and measurement views. While the conceptual view encompasses three patterns that model the externalization process, the measurement view employs certainty-factor model to empirically measure the outcome of the externalization process. Contribution: The paper contributes towards knowledge management literature in two ways. The first contribution is the Enterprise Ontology model that integrates diverse aspects of externalization. The second contribution is a Web application that validates the model through a case study in banking. Findings: The findings show that the Enterprise Ontology model and the patterns are pragmatic in externalizing the tacit knowledge of experts in a problem-solving scenario within a banking enterprise. Recommendations for Practitioners : Consider the diverse aspects (what, where, when, why, and how) during the tacit knowledge externalization process. Future Research: To extend the Enterprise Ontology model to include externalization from partially automated enterprise systems.

Aim/Purpose: This study explores how women-led SMMEs in developing countries, specifically in the Mozambican context, institutionalise e-commerce by focusing on the ongoing interaction between the SMME, its context, and process of e-commerce institutionalization. Background: It is believed that institutionalization of e-commerce provides significant benefits of unlimited access to new markets, and access to new, improved, inexpensive and convenient operational methods of transacting. Although prior studies have examined the adoption of e-commerce and the enabling and constraining factors, few have examined e-commerce (i) institutionalization (that is, post-adoption), and (ii) from a gender perspective. This study aims to respond to this paucity in the literature by exploring how women-led SMMEs in developing countries, specifically in the Mozambican context, institutionalise e-commerce. Methodology: The study follows a qualitative inquiry approach for both data collection and analysis. Semi-structured interviews were adopted for data collection and thematic analysis implemented on the data. SMMEs were purposively sampled to allow for the selection of information-rich SMMEs for study and specifically those that have gone through the experience of adoption and in some cases have institutionalized e-commerce. Contribution: The empirical findings explain how the institutionalization process from interactive e-commerce to transactive e-commerce unfolds in the Mozambican context. Findings: Transition from interactive to transactive e-commerce is firstly influenced by (i) the type of business the SMME is engaged in; and (ii) customer and trading partner’s readiness for e-commerce. Secondly, the transition process is influenced by the internal factors of (i) manager’s demographic factors; (ii) mimetic behaviour arising from exposure to (foreign) organizations in the same industry that have mature forms of e-commerce; (iii) the business networks developed with some of these organizations that have mature forms of e-commerce; (iv) access to financial resources; and (v) social media technologies. Thirdly, the process is influenced by external contextual factors of (i) limited government intervention towards e-commerce endeavors; (ii) limited to lack of financial institutions readiness for e-commerce; (iii) lack of local available IT expertise; (iv) consumer’s low purchasing power due to economic recessions; (vi) international competitive pressure; and (vii) sociocultural practices. Recommendations for Practitioners: The study provides SMME managers, practitioners, and other stakeholders concerned with women’s development with a better understanding of the process in order to develop appropriate policies and interventions that are suitable for the reality of women-led SMMEs in Mozambique and other developing countries with similar contextual characteristics. Recommendation for Researchers: The study contributes to the existing debate of e-commerce and the use of ICT for development in developing countries by providing a distinct contribution of the institutionalization process and how the contextual structures influence this process. Impact on Society: Women-led SMME managers can learn from the different experiences, and compare their e-commerce efforts with SMMEs that were able to institutionalize and make strategies for improvements within their organizations. Future Research: The manner in which women-led SMMEs employ e-commerce requires further investigation to understand how issues related to gender, the cultural context, and different regions or countries impact this process.

Aim/Purpose: This study aims to identify the main factors influencing consumers’ adoption of social commerce (s-commerce). Based on the socio-technical theory, the study suggests a research model that investigates the key social and technical factors driving consumers’ decision to purchase from social commerce websites. In addition, the research model explores the interactive relationship among these factors. Background: The phenomenon of social commerce (s-commerce) has emerged due to the increased penetration of social media and the rapid development of Web 2.0 technologies. Electronic commerce (e-commerce) companies have made significant efforts to shift their operations to s-commerce. Therefore, to facilitate their efforts to transform, various research has been conducted to investigate the main factor influencing the adoption of s-commerce. Most of these studies have emphasised the social aspects related to s-commerce design features to understand how the use of advanced web technologies influence how customers interact with each other in s-commerce environments. However, s-commerce is viewed as a socio-technical system that requires the investigation of both social and technical factors to help in the design of effective s-commerce platforms. Methodology: To validate the proposed research model, 418 paper-based and online questionnaires were collected from online shoppers in Jordan. The Structure Equation Modelling (SEM) approach was used to test the proposed hypotheses. Contribution: This study offers a research model that serves as a theoretical framework for investigating customers’ behaviour in s-commerce environment. It represents a strong context-specific model that includes both the technical and social facilitators of s-commerce. The research model participates in gaining an improved understanding of how customers’ intention, actual purchase and post-purchase experience are formed in the s-commerce environment. Findings: The results of Structure Equation Modelling (SEM) reveal that s-commerce constructs, familiarity and user experience have a positive influence on the perceived usefulness and perceived ease of use of s-commerce. In addition, perceptions of its usefulness and ease of use have a positive influence on trust, which in turn influences the purchase intention and the actual purchase. Finally, the post-purchase experience significantly influences both trust and purchase intention. Recommendations for Practitioners: This study shows that social commerce constructs strengthen customers’ perceptions of usefulness. S-commerce service providers are required to provide their customers with various channels to seek social support. Both familiarity and user experience are key enablers of customers’ perceived ease of use. S-commerce service providers consider the variation in customers’ familiarity and experience with s-commerce websites because this has a significant influence on purchase intentions and behaviour. Consequently, system designers should offer useful and sufficient information and tutorials that effectively guide customers in their searching, decision-making and purchasing activities throughout the shopping process. S-commerce service providers should understand the importance of providing secure payment systems and make their privacy policies clear to customers. Post-purchase experience has an influential role in reinforcing customers’ trust and purchase intention. The findings confirm the important role of post-purchase experience in retaining customers by improving their trust and repurchase intention. Therefore, making a customer’s post-purchase experience pleasant should be a key priority for s-commerce service providers because it has a significant influence on customers’ trust and repurchase intentions. Recommendation for Researchers: This study offers a unidimensional conceptualisation of the design features of s-commerce. These features include three main forms: recommendations and referrals, communities and forums, and reviews and ratings. Such conceptualisation provides additional insights and an understanding of the activities of information sharing in s-commerce. The significance of the technical side of s-commerce is highlighted and empirical proof is provided that social interactions guided by social technologies enhance customers’ perceived usefulness of an s-commerce website, thus increasing their trust and intention to purchase which leads to an actual purchase. This offers insights into the various types of s-commerce characteristics that contribute to facilitating customers’ purchase behaviour on s-commerce websites. Impact on Society: The findings offer insights which have important implications for research and practice to help facilitate the adoption of s-commerce. Future Research: This study considered the s-commerce websites as a homogenous online environment. Additional research could collect data from diverse online communities, such as professional groups, to provide a comprehensive understanding of how a wider variety of user behaviour is affected. Second, this was a quantitative study based on data collected in a questionnaire. Further studies may consider using qualitative or mixed methodologies (i.e. focus groups and interviews) to explore other technical and social factors that influence the use of s-commerce.

Aim/Purpose: Based on business-IT alignment, this study addresses the understudied practice of DevOps. Background: Although organizations continue to implement DevOps practices, few studies explore connections with prior theory. This study contributes to this need by developing the DevOps strategic IT alignment model. Methodology: The sample included 57 firms from the current Forbes Global 2000 and the Fortune 500 lists. The authors employed partial least squares structural equation modeling (PLS-SEM) to evaluate the DevOps IT alignment model. Contribution: The proposed model builds a foundation for further investigation into the influence of theory on DevOps using quantitative research methods. It also contributes to a reliable and valid DevOps instrument for future exploration. Findings: Continuous integration of software and knowledge sharing increases the level of IT subunit alignment in large organizations that foster DevOps. Furthermore, practicing DevOps positively influences the level of business-IT alignment. Recommendations for Practitioners: Organizations that cultivate DevOps experience greater levels of business-IT alignment through stronger knowledge sharing and continuous integration of applications. Thus, managers should identify how to develop closer bonds between subunits with dissimilar skillsets in their organizations. Recommendation for Researchers: Researchers should explore how theories interact, help, and/or do not support blossoming practices like DevOps. Impact on Society: Stronger bonds increase knowledge sharing between interdepartmental colleagues. Lower hierarchical levels of an organization as well as higher managerial levels benefit from cross-domain IT knowledge. Future Research: It is important to explore how different types of knowledge in diverse disciplines requires unique cross-discipline bonds to form and whether these relationships have connections with the contingency theory and quality management.

Aim/Purpose: Based on the social exchange theory, this study aimed to explore the underlying mechanisms and boundary conditions between organizational socialization and knowledge sharing. Background: With the advent of the era of the knowledge economy, knowledge has been replacing traditional resources such as capital, labor, and land to become the critical resources of enterprises. The competitiveness of an organization depends much on the effectiveness of its knowledge management; the success of its knowledge management largely relies upon employees’ motivation and willingness to engage in knowledge sharing. Methodology: This study is a longitudinal analysis of data collected from 281 newcomers in Chinese enterprises at two-time points with a one-month interval. Structural equation modeling (SEM) was conducted to test hypotheses by calculating standardized path coefficients and their significance levels. Contribution: The study examined models linking organizational socialization and knowledge sharing that included organizational links and sacrifice as mediators and trust as a moderator. Findings: Results show that the influences of organizational socialization on knowledge sharing change regularly over time. In the role management stage, coworker support and prospects for the future impact the practices of knowledge sharing through links and sacrifice. Moreover, the findings show that trust moderates the effect of links and sacrifice on employees’ knowledge sharing. Recommendations for Practitioners: This study can help enterprises develop targeted human resource management strategies, improve the degree of job embeddedness within the organization, and thus encourage more knowledge sharing among employees. Recommendation for Researchers: First, researchers could pay attention to more underlying mechanisms and boundary conditions in the relationship between organizational socialization and knowledge sharing. Second, focusing on specific cultural context and dimension of concepts may provide a new insight for the future study and help add greater theoretical precision to knowledge sharing. Impact on Society: First, this study suggests that coworker support and prospects for the future improve knowledge sharing within the organization. Second, understanding how job embeddedness (organizational links and organizational sacrifice) acts as a mediator enhancing knowledge sharing, managers should consider raising their attachment relationship to organizations from two aspects: links and sacrifice. Third, knowledge sharing takes place in a team-oriented context, where the success of the team requires high-quality relationships among individual team members within the team as a whole. Future Research: Researchers in the future should employ experimental research design or utilize longitudinal data to ensure that the findings reveal causation. In addition, future research can investigate how the initial level and later changes of organizational socialization are associated with knowledge sharing beyond the observational scope of traditional cross-sectional and lagged research designs.

Aim/Purpose: The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organization needs to support its operations with an automation system to improve production performance. This study investigates ERMS’s potentiality to enhance organizational performance in the oil and gas industry. Background: Oil and gas organizations generate enormous electronic records that lead to difficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information security and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology: We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents’ demographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution: This study contributes significantly to theory and practice by providing advancements in identity theory in the context of big data management and electronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data management. Findings: This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study’s findings looked at the concerns of oil and gas organizations about integrating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organizations, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the organizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners: This study contributes significantly to the theory and practice of ERMS potentiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organizations. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in protecting the credibility and authenticity of electronic records in oil and gas organizations. Recommendation for Researchers: This study serves as a foundation for future research into the function and influence of big data management on ERMS that support the organizational performance. Researchers can examine the framework of this study in other nations in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society: ERMS and its impact on BDM is still a developing field, and readers of this article can assist in gaining a better understanding of the literature’s dissemination of ERMS adoption in the oil and gas industry. This study presents an experimentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research: In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of demographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, future research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are directly related to ERMS potentiality and operational performance in the future.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background: To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology: This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user’s characteristics to predict other centers of interest. Contribution: This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings: The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners: Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendation for Researchers: Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society: The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research: The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work.

Aim/Purpose: This study aims to analyze the factors that influence user addiction to AR face filters in social network applications and their impact on the online social anxiety of users in Indonesia. Background: To date, social media users have started to use augmented reality (AR) face filters. However, AR face filters have the potential to create positive and negative effects for social media users. The study combines the Big Five Model (BFM), Sense of Virtual Community (SVOC), and Stimuli, Organism, and Response (SOR) frameworks. We adopted the SOR theory by involving the personality factors and SOVC factors as stimuli, addiction as an organism, and social anxiety as a response. BFM is the most significant theory related to personality. Methodology: We used a quantitative approach for this study by using an online survey. We conducted research on 903 Indonesian respondents who have used an AR face filter feature at least once. The respondents were grouped into three categories: overall, new users, and old users. In this study, group classification was carried out based on the development timeline of the AR face filter in the social network application. This grouping was carried out to facilitate data analysis as well as to determine and compare the different effects of the factors in each group. The data were analyzed using the covariance-based structural equation model through the AMOS 26 program. Contribution: This research fills the gap in previous research which did not discuss much about the impact of addiction in using AR face filters on online social anxiety of users of social network applications. Findings: The results of this study indicated neuroticism, membership, and immersion influence AR face filter addiction in all test groups. In addition, ARA has a significant effect on online social anxiety. Recommendations for Practitioners: The findings are expected to be valuable to social network service providers and AR creators in improving their services and to ensure policies related to the list of AR face filters that are appropriate for use by their users as a form of preventing addictive behavior of that feature. Recommendation for Researchers: This study suggested other researchers consider other negative impacts of AR face filters on aspects such as depression, life satisfaction, and academic performance. Impact on Society: AR face filter users may experience changes in their self-awareness in using face filters and avoid the latter’s negative impacts. Future Research: Future research might explore other impacts from AR face filter addiction behavior, such as depression, life satisfaction, and so on. Apart from that, future research might investigate the positive impact of AR face filters to gain a better understanding of the impact of AR face filters.

Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.